Sunil Patel

"A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks" by Shubham Vatsal and Harsh Dubey: 📝 New Research Alert: Prompt Engineering in Large Language Models 📝Exciting new paper by Shubham Vatsal and Harsh Dubey! 🌟 🔍 Title: A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Tasks 📅 Submitted: July 17, 2024 🔹 Overview: This paper provides a comprehensive survey of prompt engineering techniques used in Natural Language Processing (NLP) tasks with Large Language Models (LLMs). Prompt engineering involves composing natural language instructions (prompts) to elicit knowledge from LLMs in a structured way, without the need for extensive parameter re-training or fine-tuning. 🔹 Key Insights: Performance Gains: Prompt engineering has shown significant performance gains on various NLP tasks by leveraging the embedded knowledge of LLMs. Accessibility: The approach democratizes access to advanced NLP techniques, allowing even those without a deep mathematical machine learning background to experiment with LLMs through basic natural language conversational exchanges. Techniques and Taxonomy: The authors summarize different prompting techniques and categorize them based on the NLP tasks they are used for. They present a taxonomy diagram and discuss the state-of-the-art (SoTA) methods for specific datasets. Research Survey: The paper reviews 44 research papers discussing 39 different prompting methods applied to 29 different NLP tasks, most of which were published in the last two years. 📚 Link to Paper: Read the full paper here 👉 Why It Matters: This research highlights the importance of prompt engineering in enhancing the capabilities of LLMs without the need for extensive re-training. It democratizes access to advanced NLP techniques, making it easier for a broader audience to engage with and benefit from LLMs. #AI #MachineLearning #NLP #Research #PromptEngineering #DataScarcity

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For those who couldn't make it to SF in September for the AI Conference, check out the video link for the talk I gave on Graph RAG here 👇🏽. https://lnkd.in/gKR7AS4T The original BlackRock/Nvidia Hybrid RAG paper mentions triples (likely knowledge graphs using an RDF data model), and it's also light on implementation details of graph retrieval. Recently, I've been experimenting with strategies that utilize lightweight prompting frameworks and a reranker to combine results from graph + vector RAG, and this approach works surprisingly well even when using Kùzu's property graph data model and text2Cypher to retrieve from the graph. Give the video a watch first, and then check out the code here: https://lnkd.in/gaAWjqYv Happy to discuss more with anyone who has thoughts/comments on this methodology!

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Understanding Temperature in Language Models 🌡️ Temperature is a pivotal parameter in large language models influencing the diversity and coherence of generated text through softmax-based sampling. Temperature is a parameter used in softmax-based sampling methods within language models. In essence, softmax converts logits (raw model outputs) into probabilities. Temperature scales these probabilities before sampling from them. The formula for temperature scaling is: Scaled logits = logits / T, where T is the temperature parameter. Higher temperatures lead to more uniform distributions over the vocabulary, encouraging the model to explore diverse word choices. Conversely, lower temperatures sharpen the distribution, favoring high-confidence predictions and reducing diversity in generated text. Diversity vs. Coherence: High Temperature: Increases the entropy of the probability distribution, encouraging the model to explore a wider range of word choices. This enhances creativity in generated text. Low Temperature: Decreases entropy, favoring high-confidence predictions and reducing randomness. This improves coherence and ensures more predictable outputs. Understanding temperature empowers researchers and developers to optimize language model outputs for specific applications, striking a balance between creativity and coherence. Let's delve deeper into the nuances of AI and NLP together! 💡 #NLP #AI #MachineLearning #DataScience #NaturalLanguageProcessing #LLMs Feel free to connect and share your experiences or ask any questions! 🙌

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👉🏼 Expansive data, extensive model: Investigating discussion topics around LLM through unsupervised machine learning in academic papers and news 🤓 Hae Sun Jung 👇🏻 https://lnkd.in/ef_MF54v 🔍 Focus on data insights: - Utilized data from Web of Science and LexisNexis to explore topic modeling methods for Large Language Models (LLMs). - Evaluated various topic modeling approaches such as LDA, NMF, CTM, and BERTopic based on diversity and coherence metrics. - BERTopic demonstrated superior performance in diversity and coherence across both platforms. 💡 Main outcomes and implications: - News articles covered a wide range of topics related to LLM applications, focusing on specialized domains. - Research papers concentrated on technical aspects of LLM technology. - Insights gained can help understand future challenges and opportunities for LLMs in different sectors. 📚 Field significance: - Enhances understanding of how LLMs are utilized in academic and news contexts. - Provides insights for enterprises leveraging LLMs for service delivery. 🗄️: #topicmodeling #unsupervisedlearning #machinelearning #datainsights

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Both AI research and engineering use Pytorch, but there is not a single shared library between the RAG and agent research and engineering communities. I have read hundreds of papers on RAG and agents, almost none of which use frameworks like Langchain and LlamaIndex. I have talked to many ML engineers building RAG and agent products; most people only use the utility functions of these tools like text splitters or output parsers. The majority of people who use these two popular frameworks are software engineers who don't have much background in AI. Building RAG and agent applications is no different from training a model; you just currently manually tune your prompts. You need evaluations, you need iterations, you need to get the in-context learning working well and gather more data to automate your prompting process and to move into model finetuning. The problem with researchers’ code is that it has no abstraction at all. It only works on the benchmarking dataset. They lack powerful string processing that can generalize the tool calls. Their code is hard to read as it is not organized. Thus, it is really hard to adapt to production. The problems with the frameworks are that they have abstracted so many layers but are not supported with really strong engineering to provide robust adaptation to different use cases. They are more like no-code solutions, which are perfect for people to use out-of-the-box, but terrible for products and research. There can be a solution great for both the ML research and the product community: a library that provides strong string processing, an easy-to-understand tool interface to support tool calls, various output formats, and various access to different models with great monitoring capabilities like TensorBoard for Pytorch. That is the mission and purpose of LightRAG. Let researchers and engineers work on what matters: prompts, datasets, evaluations, and model finetuning without building everything from scratch alone with subpar solutions or using an overly engineered framework which requires them to spend more time figuring out how to customize instead of on what matters. I know I'm not a popular voice, but this is super important, and I think the majority of the community is moving in the wrong direction, even some of those people I looked up to and considered the best in the field. If you are passionate about a library that really unites RAG and Agent research and engineering, please work with me. I know I'm not able to do this alone.

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🚨Paper Alert 🚨 ➡️Paper Title: A Multimodal Automated Interpretability Agent 🌟Few pointers from the paper 🎯In this paper authors have described MAIA, “a  🤖 Multimodal Automated Interpretability Agent 🤖”. MAIA is a system that uses neural models to automate neural model understanding tasks like feature interpretation and failure mode discovery. 🎯It equips a pre-trained vision-language model with a set of tools that support iterative experimentation on subcomponents of other models to explain their behavior. 🎯These include tools commonly used by human interpretability researchers: for synthesizing and editing inputs, computing maximally activating exemplars from real-world datasets, and summarizing and describing experimental results. 🎯Interpretability experiments proposed by MAIA compose these tools to describe and explain system behavior. 🎯Authors evaluated  applications of MAIA to computer vision models. They first characterize MAIA’s ability to describe (neuron-level) features in learned representations of images. 🎯Across several trained models and a novel dataset of synthetic vision neurons with paired ground-truth descriptions, MAIA produces descriptions comparable to those generated by expert human experimenters. 🎯Then Authors showed that MAIA can aid in two additional interpretability tasks: reducing sensitivity to spurious features, and automatically identifying inputs likely to be mis-classified. 🏢Organization:  MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) 🧙Paper Authors: Tamar Rott Shaham, Sarah Schwettmann, Franklin Wang, Achyuta Rajaram, Evan Hernandez, Jacob Andreas , Antonio Torralba 1️⃣Read the Full Paper here: https://lnkd.in/gjmN_gaR 2️⃣Project Page: https://lnkd.in/gxPr_jpM 3️⃣Experiment Browser: https://lnkd.in/gGe7FyUY 🎥 Be sure to watch the attached Demo Video-Sound on 🔊🔊 Music by Sergii Pavkin from Pixabay Find this Valuable 💎 ? ♻️REPOST and teach your network something new Follow me 👣, Naveen Manwani, for the latest updates on Tech and AI-related news, insightful research papers, and exciting announcements.

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Shashank Sonkar and colleagues are back with another great paper and matching GitHub repo, "Pedagogical Alignment of Large Language Models". You can read the paper on arXiv here - https://lnkd.in/enZfCJgi, and browse the code here - https://lnkd.in/gyW5hSzp. They demonstrate an interesting approach to generating synthetic data to train open weights models to behave more pedagogically, as well as developing some new evaluation metrics. I think there's lots here for those of us interested in this kind of work to learn from and build on. (If synthetic data generation is new do you, check out this post from answer.ai earlier this week - https://lnkd.in/ecsG7R4b.)

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Here's a basic implementation of a Variational Autoencoder (VAE) from scratch using PyTorch: *VAE Model* ``` import torch import torch.nn as nn import torch.optim as optim from torch.distributions import Normal class VAE(nn.Module): def __init__(self, latent_dim, hidden_dim, input_dim): super(VAE, self).__init__() self.encoder = nn.Sequential( nn.Linear(input_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, latent_dim*2) ) self.decoder = nn.Sequential( nn.Linear(latent_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, hidden_dim), nn.ReLU(), nn.Linear(hidden_dim, input_dim) ) def encode(self, x): z_mean, z_log_var = self.encoder(x).chunk(2, dim=1) return z_mean, z_log_var def reparameterize(self, z_mean, z_log_var): std = torch.exp(0.5 * z_log_var) eps = torch.randn_like(std) z = z_mean + eps * std return z def decode(self, z): return self.decoder(z) def forward(self, x): z_mean, z_log_var = self.encode(x) z = self.reparameterize(z_mean, z_log_var) x_reconstructed = self.decode(z) return x_reconstructed, z_mean, z_log_var def loss_function(self, x, x_reconstructed, z_mean, z_log_var): reconstruction_loss = ((x - x_reconstructed) ** 2).sum(dim=1).mean() kl_loss = -0.5 * (1 + z_log_var - z_mean**2 - torch.exp(z_log_var)).sum(dim=1).mean() return reconstruction_loss + kl_loss ``` *Training Loop* ``` vae = VAE(latent_dim=2, hidden_dim=256, input_dim=784) optimizer = optim.Adam(vae.parameters(), lr=0.001) for epoch in range(100): for x in train_loader: x = x.view(-1, 784) x_reconstructed, z_mean, z_log_var = vae(x) loss = vae.loss_function(x, x_reconstructed, z_mean, z_log_var) optimizer.zero_grad() loss.backward() optimizer.step() print(f'Epoch {epoch+1}, Loss: {loss.item()}') ``` *Example Use Case* To use this VAE, you can train it on a dataset like MNIST, and then use it to generate new samples or perform anomaly detection. For example, to generate new samples, you can sample from the latent space and pass the samples through the decoder: ``` z = torch.randn(1, 2) x_reconstructed = vae.decode(z) ``` This will generate a new sample that is similar to the training data. Note that this is a basic implementation, and you may want to add additional features such as batch normalization, convolutional layers, or more complex architectures. Additionally, you can experiment with different loss functions, optimizers, and hyperparameters to improve the performance of the VAE.

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Very useful paper to read on #LLM in #healthtech, #digitalhealth, and clinical care. Thank you, Jan Beger for all your excellence and informative summaries of #AI in #healthcare papers!

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👉🏼 Large language models in neurosurgery: a systematic review and meta-analysis 🤓 Advait Patil 👇🏻 https://lnkd.in/emhcKebM 🔍 Focus on data insights: - 📊 A total of 51 articles were included, indicating a growing interest in the application of large language models

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